Translation

Files in /eval, /extra, & /learn - comments translated from Japanese to English

src/eval/nnue/layers/sum.h

@@ -1,4 +1,4 @@
-// NNUE評価関数の層Sumの定義
+// Definition of layer Sum of NNUE evaluation function
 
 #ifndef _NNUE_LAYERS_SUM_H_
 #define _NNUE_LAYERS_SUM_H_
@@ -13,7 +13,7 @@ namespace NNUE {
 
 namespace Layers {
 
-// 複数の層の出力の和を取る層
+// Layer that sums the output of multiple layers
 template <typename FirstPreviousLayer, typename... RemainingPreviousLayers>
 class Sum : public Sum<RemainingPreviousLayers...> {
  private:
@@ -21,25 +21,25 @@ class Sum : public Sum<RemainingPreviousLayers...> {
   using Tail = Sum<RemainingPreviousLayers...>;
 
  public:
-  // 入出力の型
+  // Input/output type
   using InputType = typename Head::OutputType;
   using OutputType = InputType;
   static_assert(std::is_same<InputType, typename Tail::InputType>::value, "");
 
-  // 入出力の次元数
+  // number of input/output dimensions
   static constexpr IndexType kInputDimensions = Head::kOutputDimensions;
   static constexpr IndexType kOutputDimensions = kInputDimensions;
-  static_assert(kInputDimensions == Tail::kInputDimensions , "");
+  static_assert(kInputDimensions == Tail::kInputDimensions ,"");
 
-  // この層で使用する順伝播用バッファのサイズ
+  // Size of forward propagation buffer used in this layer
   static constexpr std::size_t kSelfBufferSize =
       CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
 
-  // 入力層からこの層までで使用する順伝播用バッファのサイズ
+  // Size of the forward propagation buffer used from the input layer to this layer
   static constexpr std::size_t kBufferSize =
       std::max(Head::kBufferSize + kSelfBufferSize, Tail::kBufferSize);
 
-  // 評価関数ファイルに埋め込むハッシュ値
+  // Hash value embedded in the evaluation function file
   static constexpr std::uint32_t GetHashValue() {
     std::uint32_t hash_value = 0xBCE400B4u;
     hash_value ^= Head::GetHashValue() >> 1;
@@ -49,67 +49,67 @@ class Sum : public Sum<RemainingPreviousLayers...> {
     return hash_value;
   }
 
-  // 入力層からこの層までの構造を表す文字列
+  // A string that represents the structure from the input layer to this layer
   static std::string GetStructureString() {
     return "Sum[" +
         std::to_string(kOutputDimensions) + "](" + GetSummandsString() + ")";
   }
 
-  // パラメータを読み込む
+  // read parameters
   bool ReadParameters(std::istream& stream) {
     if (!Tail::ReadParameters(stream)) return false;
     return previous_layer_.ReadParameters(stream);
   }
 
-  // パラメータを書き込む
+  // write parameters
   bool WriteParameters(std::ostream& stream) const {
     if (!Tail::WriteParameters(stream)) return false;
     return previous_layer_.WriteParameters(stream);
   }
 
-  // 順伝播
+  // forward propagation
   const OutputType* Propagate(
       const TransformedFeatureType* transformed_features, char* buffer) const {
     Tail::Propagate(transformed_features, buffer);
     const auto head_output = previous_layer_.Propagate(
         transformed_features, buffer + kSelfBufferSize);
     const auto output = reinterpret_cast<OutputType*>(buffer);
-    for (IndexType i = 0; i < kOutputDimensions; ++i) {
+    for (IndexType i = 0; i <kOutputDimensions; ++i) {
       output[i] += head_output[i];
     }
     return output;
   }
 
  protected:
-  // 和を取る対象となる層のリストを表す文字列
+  // A string that represents the list of layers to be summed
   static std::string GetSummandsString() {
     return Head::GetStructureString() + "," + Tail::GetSummandsString();
   }
 
-  // 学習用クラスをfriendにする
+  // Make the learning class a friend
   friend class Trainer<Sum>;
 
-  // この層の直前の層
+  // the layer immediately before this layer
   FirstPreviousLayer previous_layer_;
 };
 
-// 複数の層の出力の和を取る層（テンプレート引数が1つの場合）
+// Layer that sums the output of multiple layers (when there is one template argument)
 template <typename PreviousLayer>
 class Sum<PreviousLayer> {
  public:
-  // 入出力の型
+  // Input/output type
   using InputType = typename PreviousLayer::OutputType;
   using OutputType = InputType;
 
-  // 入出力の次元数
+  // number of input/output dimensions
   static constexpr IndexType kInputDimensions =
       PreviousLayer::kOutputDimensions;
   static constexpr IndexType kOutputDimensions = kInputDimensions;
 
-  // 入力層からこの層までで使用する順伝播用バッファのサイズ
+  // Size of the forward propagation buffer used from the input layer to this layer
   static constexpr std::size_t kBufferSize = PreviousLayer::kBufferSize;
 
-  // 評価関数ファイルに埋め込むハッシュ値
+  // Hash value embedded in the evaluation function file
   static constexpr std::uint32_t GetHashValue() {
     std::uint32_t hash_value = 0xBCE400B4u;
     hash_value ^= PreviousLayer::GetHashValue() >> 1;
@@ -117,38 +117,38 @@ class Sum<PreviousLayer> {
     return hash_value;
   }
 
-  // 入力層からこの層までの構造を表す文字列
+  // A string that represents the structure from the input layer to this layer
   static std::string GetStructureString() {
     return "Sum[" +
         std::to_string(kOutputDimensions) + "](" + GetSummandsString() + ")";
   }
 
-  // パラメータを読み込む
+  // read parameters
   bool ReadParameters(std::istream& stream) {
     return previous_layer_.ReadParameters(stream);
   }
 
-  // パラメータを書き込む
+  // write parameters
   bool WriteParameters(std::ostream& stream) const {
     return previous_layer_.WriteParameters(stream);
   }
 
-  // 順伝播
+  // forward propagation
   const OutputType* Propagate(
       const TransformedFeatureType* transformed_features, char* buffer) const {
     return previous_layer_.Propagate(transformed_features, buffer);
   }
 
  protected:
-  // 和を取る対象となる層のリストを表す文字列
+  // A string that represents the list of layers to be summed
   static std::string GetSummandsString() {
     return PreviousLayer::GetStructureString();
   }
 
-  // 学習用クラスをfriendにする
+  // Make the learning class a friend
   friend class Trainer<Sum>;
 
-  // この層の直前の層
+  // the layer immediately before this layer
   PreviousLayer previous_layer_;
 };
 
@@ -160,4 +160,4 @@ class Sum<PreviousLayer> {
 
 #endif  // defined(EVAL_NNUE)
 
-#endif
+#endif